Machine Learning Engineer, AWS Applied AI Solution

As part of the AWS Applied AI Solutions organization, our vision is to provide business applications, leveraging Amazon's unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We accelerate our customers' businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. Our team combines Amazon's real-world experience with state-of-the art AI to create opinionated, turnkey solutions that are no-brainers to buy and easy to use.

We're building applied AI solutions that businesses love and trust. Our ambition is to become the partner companies rely on to run their business every day - putting AI to work to deliver better customer experiences, operational excellence, and faster innovation. We're a fast-moving, scrappy team building a new agentic product from the ground up. If bias for action is your favorite leadership principle, you'll fit right in.

The Role
We're seeking a talented Machine Learning Engineer with expertise in agentic system, production ML systems, and scalable deployment
architectures. You'll bridge the gap between state-of-art research and customer-facing products, contribute to our collaborative and innovative culture, and deliver production-ready ML solutions that raise the bar for the entire team.

What You'll Do
• Work closely with Applied Scientists and cross-functional engineering teams to transform research code into robust, scalable production systems
• Own end-to-end deployment at scale of Generative AI and ML methods, ensuring reliability and performance
• Establish scalable, efficient, automated processes for large-scale data analysis, machine learning model development, model validation and serving
• Research and implement innovative approaches for efficient model deployment, training, and optimization
• Document processes and methods for both technical and non-technical audiences, ensuring knowledge transfer and best practices
• Contribute to code reviews and maintain high engineering standards across the team
• Mentor junior MLEs and actively participate in recruiting top talent to grow
the team
• Present outcomes and explain technical approaches to senior leadership, translating complex concepts into business impact